






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase								=		{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase								=		["MobileViTFeatureExtractor"]
							UpperCamelCase								=		["MobileViTImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase								=		[
							    "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MobileViTForImageClassification",
							    "MobileViTForSemanticSegmentation",
							    "MobileViTModel",
							    "MobileViTPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase								=		[
							    "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFMobileViTForImageClassification",
							    "TFMobileViTForSemanticSegmentation",
							    "TFMobileViTModel",
							    "TFMobileViTPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilevit import MobileViTFeatureExtractor
														from .image_processing_mobilevit import MobileViTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilevit import (
														    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileViTForImageClassification,
														    MobileViTForSemanticSegmentation,
														    MobileViTModel,
														    MobileViTPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mobilevit import (
														    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFMobileViTForImageClassification,
														    TFMobileViTForSemanticSegmentation,
														    TFMobileViTModel,
														    TFMobileViTPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase_    :			int    =	16
lowerCamelCase_    :			str    =	32







def 							A__       (    lowerCamelCase    )	->						int:
				return int(x / 2**20    )

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __enter__(							self			:							int   ):
							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							UpperCamelCase_:				str						=    torch.cuda.memory_allocated()
							return self




			def __exit__(							self			:							List[Any]    ,						*snake_case_			:							Union[str, Any]   ):
							gc.collect()
							torch.cuda.empty_cache()
							UpperCamelCase_:				List[str]						=    torch.cuda.memory_allocated()
							UpperCamelCase_:				int						=    torch.cuda.max_memory_allocated()
							UpperCamelCase_:				Optional[int]						=    bamb(self.end - self.begin   )
							UpperCamelCase_:				Tuple						=    bamb(self.peak - self.begin   )
							# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 							A__       (    lowerCamelCase  ,     lowerCamelCase = 16  ,     lowerCamelCase = "bert-base-cased"  ,     lowerCamelCase = 3_20  ,     lowerCamelCase = 1_60  ,     )	->						Dict:
				UpperCamelCase_:				str						=    AutoTokenizer.from_pretrained(lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    load_dataset(
				    """glue"""  ,     """mrpc"""  ,     split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''}    )

				def tokenize_function(lowerCamelCase    ):
								# max_length=None => use the model max length (it's actually the default)
								UpperCamelCase_:				Optional[Any]						=    tokenizer(examples["""sentence1"""]  ,     examples["""sentence2"""]  ,     truncation=lowerCamelCase  ,     max_length=lowerCamelCase    )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				UpperCamelCase_:				Dict						=    datasets.map(
				    lowerCamelCase  ,     batched=lowerCamelCase  ,     remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,     load_from_cache_file=lowerCamelCase    )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				UpperCamelCase_:				int						=    tokenized_datasets.rename_column("""label"""  ,     """labels"""    )

				def collate_fn(lowerCamelCase    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(lowerCamelCase  ,     padding="""max_length"""  ,     max_length=1_28  ,     return_tensors="""pt"""    )
								return tokenizer.pad(lowerCamelCase  ,     padding="""longest"""  ,     return_tensors="""pt"""    )

				# Instantiate dataloaders.
				UpperCamelCase_:				List[str]						=    DataLoader(
				    tokenized_datasets["""train"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )
				UpperCamelCase_:				Optional[Any]						=    DataLoader(
				    tokenized_datasets["""validation"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )

				return train_dataloader, eval_dataloader







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				# Initialize accelerator
				UpperCamelCase_:				Any						=    Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				UpperCamelCase_:				str						=    config["""lr"""]
				UpperCamelCase_:				Any						=    int(config["""num_epochs"""]    )
				UpperCamelCase_:				Tuple						=    int(config["""seed"""]    )
				UpperCamelCase_:				Dict						=    int(config["""batch_size"""]    )
				UpperCamelCase_:				Dict						=    args.model_name_or_path

				set_seed(lowerCamelCase    )
				UpperCamelCase_, UpperCamelCase_:				int						=    get_dataloaders(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     args.n_train  ,     args.n_val    )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCamelCase_:				Any						=    AutoModelForSequenceClassification.from_pretrained(lowerCamelCase  ,     return_dict=lowerCamelCase    )

				# Instantiate optimizer
				UpperCamelCase_:				Tuple						=    (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				UpperCamelCase_:				List[Any]						=    optimizer_cls(params=model.parameters()  ,     lr=lowerCamelCase    )

				if accelerator.state.deepspeed_plugin is not None:
								UpperCamelCase_:				Any						=    accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								UpperCamelCase_:				List[Any]						=    1
				UpperCamelCase_:				Optional[int]						=    (len(lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								UpperCamelCase_:				List[Any]						=    get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase  ,     num_warmup_steps=0  ,     num_training_steps=lowerCamelCase  ,     )
				else:
								UpperCamelCase_:				Dict						=    DummyScheduler(lowerCamelCase  ,     total_num_steps=lowerCamelCase  ,     warmup_num_steps=0    )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				str						=    accelerator.prepare(
				    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

				# We need to keep track of how many total steps we have iterated over
				UpperCamelCase_:				str						=    0
				# We also need to keep track of the stating epoch so files are named properly
				UpperCamelCase_:				Dict						=    0

				# Now we train the model
				UpperCamelCase_:				Dict						=    {}
				for epoch in range(lowerCamelCase  ,     lowerCamelCase    ):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(lowerCamelCase    ):
																UpperCamelCase_:				Union[str, Any]						=    model(**lowerCamelCase    )
																UpperCamelCase_:				str						=    outputs.loss
																UpperCamelCase_:				List[str]						=    loss / gradient_accumulation_steps
																accelerator.backward(lowerCamelCase    )
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

																overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin    )    )    )
								accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used    )    )
								accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked    )    )
								accelerator.print(
								    """Total Peak Memory consumed during the train (max): {}""".format(
								        tracemalloc.peaked + bamb(tracemalloc.begin    )    )    )
								UpperCamelCase_:				Optional[Any]						=    tracemalloc.peaked + bamb(tracemalloc.begin    )
								if args.peak_memory_upper_bound is not None:
												assert (
												    train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
												), "Peak memory usage exceeded the upper bound"

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir  ,     """peak_memory_utilization.json"""    )  ,     """w"""    ) as f:
												json.dump(lowerCamelCase  ,     lowerCamelCase    )







def 							A__       (    )	->						Optional[Any]:
				UpperCamelCase_:				Any						=    argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
				parser.add_argument(
				    """--model_name_or_path"""  ,     type=lowerCamelCase  ,     default="""bert-base-cased"""  ,     help="""Path to pretrained model or model identifier from huggingface.co/models."""  ,     required=lowerCamelCase  ,     )
				parser.add_argument(
				    """--output_dir"""  ,     type=lowerCamelCase  ,     default="""."""  ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""  ,     )
				parser.add_argument(
				    """--peak_memory_upper_bound"""  ,     type=lowerCamelCase  ,     default=lowerCamelCase  ,     help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""  ,     )
				parser.add_argument(
				    """--n_train"""  ,     type=lowerCamelCase  ,     default=3_20  ,     help="""Number of training examples to use."""  ,     )
				parser.add_argument(
				    """--n_val"""  ,     type=lowerCamelCase  ,     default=1_60  ,     help="""Number of validation examples to use."""  ,     )
				parser.add_argument(
				    """--num_epochs"""  ,     type=lowerCamelCase  ,     default=1  ,     help="""Number of train epochs."""  ,     )
				UpperCamelCase_:				Optional[int]						=    parser.parse_args()
				UpperCamelCase_:				Union[str, Any]						=    {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
				training_function(lowerCamelCase  ,     lowerCamelCase    )


if __name__ == "__main__":
							main()






